Explore machine learning concepts related to embeddings. Clear explanations and practical insights.
Understanding complete, dimensional, and cluster collapse — the failure modes that every self-supervised method must prevent. Learn why collapse happens and how contrastive, asymmetric, regularization, and masking approaches solve it.
Compare all approximate nearest neighbor algorithms side-by-side: HNSW, IVF-PQ, LSH, Annoy, and ScaNN. Find the best approach for your use case.
Interactive visualization of HNSW - the graph-based algorithm that powers modern vector search with logarithmic complexity.
Explore the fundamental data structures powering vector databases: trees, graphs, hash tables, and hybrid approaches for efficient similarity search.
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
Explore how LSH uses probabilistic hash functions to find similar vectors in sub-linear time, perfect for streaming and high-dimensional data.
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
Interactive visualization of high-dimensional vector spaces, word relationships, and semantic arithmetic operations.
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
Embedding quantization simulator: explore memory-accuracy trade-offs from float32 to int8 and binary representations for retrieval.
The modality gap in CLIP and vision-language models: why image and text embeddings occupy separate regions despite contrastive training.